Module 0
University of South Florida
Learn the fundamental technology in FinTech
A “Hands on” course
Lab sessions and exercises in the class
Emphasize on building practical skills (and good habits)
Process and Summarize Financial Data
Practical knowledge in how ML algorithms work
Matthew Son, Ph.D. in Finance
Office: BSN 3127
gson@usf.edu
R, Python, C/C++
Research area:
Please tell us about yourself briefly:
The goal of this course is:
and ultimately:
Throughout the course you’ll use and learn:
R programming language and packages
VScode IDE for main interface
Quarto for technical documentation
h2o.ai for ML algorithm implementations
Copilot coding agent tools for programming
Basic knowledge in Investments (Asset pricing) and Corporate Finance
Excel & Financial calculator
General proficiency with computers
Some experience in any programming
A two-part structure: lectures and lab sessions
Lecture: the instructor will cover the concepts and demonstrate the workflow in R.
Lab sessions:
In person is preferred over email.
When writing in email, include:
No required textbook. Lecture notes and supplementary materials will be provided throughout the course.
1. John C. Hull, “Machine Learning in Business: An Introduction to the World of Data Science”, 3rd edition, 2021, GFS Press. ISBN-13: “979-8508489441”
2. Darren Cook, “Practical Machine Learning with H2O”, 1st edition, 2017, OReilly. ISBN-13: “978-1491964606”
3. “Python Polars: The Definitive Guide”, 1st edition, 2023, OReilly. ISBN-13: “978-1098156084”
4. Hadley Wickham & Garrett Grolemund, R for Data Science, 2nd edition, 2023, OReilly. ISBN-13: “978-1491910399”. Electronic copies are available for free.
The latest stable version of R and VScode.
Please bring your laptop (macOS/Windows/Linux with popular distro)
| Graded Items | Percent of Final Grade |
|---|---|
| Participation | 10% |
| ML Assignment | 15% |
| In-Class Quizzes | 30% |
| Lab Problems | 15% |
| Final Exam | 30% |
Module 0: The FinTech Landscape
Module 1: Financial analysis with R
Module 2: Introduction to Big data analytics
Module 3: Unsupervised Learning
Module 4: Supervised Learning
The course schedule is tentative and subject to change.
| Week | Topics | Finance Applications | |
|---|---|---|---|
| 1 ~ 4 | R & Quarto |
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| 5 ~ 7 | Big data |
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| 8 ~ 10 | Unsupervised Learning |
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| 11 ~ 15 | Supervised Learning |
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Attendance is expected.
In-class quizzes will be administered through Canvas with Honorlock monitoring.
Closed book, 1 page of cheat sheet (can be typed) is allowed.
You will be provided a financial dataset.
Explore, visualize and provide insights about the data
Apply machine learning to develop a model and evaluate
Document should be generated using Quarto.
No late submissions will be accepted.
In persaon Canvas Exam and Honorlock screen monitoring
No AI tools are allowed
Closed book
A letter-size, double-sided cheat sheet will be allowed.
2 hours
Ensure not to disrupt the course by talking, arriving late, eating, etc.
Please limit computer usage to activities directly related to the class.
Phones are not permitted as they are unlikely to be useful for course-related activities.
I won’t grade late submissions except:
- if a valid excuse is communicated to the instructor before the deadline
- valid excuses with proof will be accepted later, in extenuating circumstances
A valid excuse must be communicated to the instructor before the exam/quiz
Students may request re-grading exams and assignments within one week (seven calendar days) after grading.
In the case of a regrading request after the final exam, all previous submissions for the course will be thoroughly reevaluated to ensure consistent grading standards.
FIN4773: Big Data and Machine Learning in Finance